SoroushVahidi/scidocs huggingface dataset:
Striving for the moral self: the effects of recalling past moral actions on future moral behavior.
(2011)

People's desires to see themselves as moral actors can contribute to their striving for and achievement of a sense of self-completeness. The authors use self-completion theory to predict (and show) that recalling one's own (im)moral behavior leads to compensatory rather than consistent moral action as a way of completing the moral self. In three studies, people who recalled their immoral behavior reported greater participation in moral activities (Study 1), reported stronger prosocial intentions (Study 2), and showed less cheating (Study 3) than people who recalled their moral behavior. These compensatory effects were related to the moral magnitude of the recalled event, but they did not emerge when people recalled their own positive or negative nonmoral behavior (Study 2) or others' (im)moral behavior (Study 3). Thus, the authors extend self-completion theory to the moral domain and use it to integrate the research on moral cleansing (remunerative moral strivings) and moral licensing (relaxed moral strivings).
Transcriptional reprogramming in yeast using dCas9 and combinatorial gRNA strategies
(2017)

BACKGROUND
Transcriptional reprogramming is a fundamental process of living cells in order to adapt to environmental and endogenous cues. In order to allow flexible and timely control over gene expression without the interference of native gene expression machinery, a large number of studies have focused on developing synthetic biology tools for orthogonal control of transcription. Most recently, the nuclease-deficient Cas9 (dCas9) has emerged as a flexible tool for controlling activation and repression of target genes, by the simple RNA-guided positioning of dCas9 in the vicinity of the target gene transcription start site.


RESULTS
In this study we compared two different systems of dCas9-mediated transcriptional reprogramming, and applied them to genes controlling two biosynthetic pathways for biobased production of isoprenoids and triacylglycerols (TAGs) in baker's yeast Saccharomyces cerevisiae. By testing 101 guide-RNA (gRNA) structures on a total of 14 different yeast promoters, we identified the best-performing combinations based on reporter assays. Though a larger number of gRNA-promoter combinations do not perturb gene expression, some gRNAs support expression perturbations up to ~threefold. The best-performing gRNAs were used for single and multiplex reprogramming strategies for redirecting flux related to isoprenoid production and optimization of TAG profiles. From these studies, we identified both constitutive and inducible multiplex reprogramming strategies enabling significant changes in isoprenoid production and increases in TAG.


CONCLUSION
Taken together, we show similar performance for a constitutive and an inducible dCas9 approach, and identify multiplex gRNA designs that can significantly perturb isoprenoid production and TAG profiles in yeast without editing the genomic context of the target genes. We also identify a large number of gRNA positions in 14 native yeast target pomoters that do not affect expression, suggesting the need for further optimization of gRNA design tools and dCas9 engineering.
Expertise-related deactivation of the right temporoparietal junction during musical improvisation
(2010)

Musical training has been associated with structural changes in the brain as well as functional differences in brain activity when musicians are compared to nonmusicians on both perceptual and motor tasks. Previous neuroimaging comparisons of musicians and nonmusicians in the motor domain have used tasks involving prelearned motor sequences or synchronization with an auditorily presented sequence during the experiment. Here we use functional magnetic resonance imaging (fMRI) to examine expertise-related differences in brain activity between musicians and nonmusicians during improvisation--the generation of novel musical-motor sequences--using a paradigm that we previously used in musicians alone. Despite behaviorally matched performance, the two groups showed significant differences in functional brain activity during improvisation. Specifically, musicians deactivated the right temporoparietal junction (rTPJ) during melodic improvisation, while nonmusicians showed no change in activity in this region. The rTPJ is thought to be part of a ventral attentional network for bottom-up stimulus-driven processing, and it has been postulated that deactivation of this region occurs in order to inhibit attentional shifts toward task-irrelevant stimuli during top-down, goal-driven behavior. We propose that the musicians' deactivation of the rTPJ during melodic improvisation may represent a training-induced shift toward inhibition of stimulus-driven attention, allowing for a more goal-directed performance state that aids in creative thought.
With a flick of the wrist: stretch sensors as lightweight input for mobile devices
(2012)

With WristFlicker, we detect wrist movement through sets of stretch sensors embedded in clothing. Our system supports wrist rotation (pronation/supination), and both wrist tilts (flexion/extension and ulnar/radial deviation). Each wrist movement is measured by two opposing stretch sensors, mimicking the counteracting movement of muscles. We discuss interaction techniques that allow a user to control a music player through this lightweight input.
Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content
(2015)

We consider the problem of learning distributed representations for documents in data streams. The documents are represented as low-dimensional vectors and are jointly learned with distributed vector representations of word tokens using a hierarchical framework with two embedded neural language models. In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them. The models learn continuous vector representations for both word tokens and documents such that semantically similar documents and words are close in a common vector space. We discuss extensions to our model, which can be applied to personalized recommendation and social relationship mining by adding further user layers to the hierarchy, thus learning user-specific vectors to represent individual preferences. We validated the learned representations on a public movie rating data set from MovieLens, as well as on a large-scale Yahoo News data comprising three months of user activity logs collected on Yahoo servers. The results indicate that the proposed model can learn useful representations of both documents and word tokens, outperforming the current state-of-the-art by a large margin.
IEC 61850 substation configuration language as a basis for automated security and SDN configuration
(2017)

IEC61850 has revolutionized the way substations are configured and maintained. Substation Configuration Language (SCL) defines the parameters needed to configure individual devices and combine them into a working system. Security is implemented by IEC62351 and there are potential vulnerabilities. Best practice recommendations are for defense in depth. SCL contains sufficient information to auto-configure network equipment, firewalls, IDS and SDN based networks.
Learning to Navigate for Fine-Grained Classification
(2018)

Fine-grained classification is challenging due to the difficulty of finding discriminative features. Finding those subtle traits that fully characterize the object is not straightforward. To handle this circumstance, we propose a novel self-supervision mechanism to effectively localize informative regions without the need of bounding-box/part annotations. Our model, termed NTS-Net for Navigator-Teacher-Scrutinizer Network, consists of a Navigator agent, a Teacher agent and a Scrutinizer agent. In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher. After that, the Scrutinizer scrutinizes the proposed regions from Navigator and makes predictions. Our model can be viewed as a multi-agent cooperation, wherein agents benefit from each other, and make progress together. NTS-Net can be trained end-to-end, while provides accurate fine-grained classification predictions as well as highly informative regions during inference. We achieve state-of-the-art performance in extensive benchmark datasets.
Understanding predictability and exploration in human mobility
(2017)

Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.
Detection of hiding in the least significant bit
(2003)

In this paper, we apply the theory of hypothesis testing to the steganalysis, or detection of hidden data, in the least significant bit (LSB) of a host image. The hiding rate (if data is hidden) and host probability mass function (PMF) are unknown. Our main results are as follows. a) Two types of tests are derived: a universal (over choices of host PMF) method that has certain asymptotic optimality properties and methods that are based on knowledge or estimation of the host PMF and, hence, an appropriate likelihood ratio (LR). b) For a known host PMF, it is shown that the composite hypothesis testing problem corresponding to an unknown hiding rate reduces to a worst-case simple hypothesis testing problem. c) Using the results for a known host PMF, practical tests based on the estimation of the host PMF are obtained. These are shown to be superior to the state of the art in terms of receiver operating characteristics as well as self-calibration across different host images. Estimators for the hiding rate are also developed.
An Efficient Algorithm of Frequent Itemsets Mining Based on MapReduce
(2014)

Mainstream parallel algorithms for mining frequent itemsets (patterns) were designed by implementing FP-Growth or Apriori algorithms on MapReduce (MR) framework. Existing MR FP-Growth algorithms can not distribute data equally among nodes, and MR Apriori algorithms utilize multiple map/reduce procedures and generate too many key-value pairs with value of 1; these disadvantages hinder their performance. This paper proposes an algorithm FIMMR: it firstly mines local frequent itemsets for each data chunk as candidates, applies prune strategies to the candidates, and then identifies global frequent itemsets from candidates. Experimental results show that the time efficiency of FIMMR outperforms PFP and SPC significantly; and under small minimum support threshold, FIMMR can achieve one order of magnitude improvement than the other two algorithms; meanwhile, the speedup of FIMMR is also satisfactory.
Visual Analysis of Public Utility Service Problems in a Metropolis
(2014)

Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.
Bootstrapping Biomedical Ontologies for Scientific Text using NELL
(2012)

We describe an open information extraction system for biomedical text based on NELL (the NeverEnding Language Learner) [7], a system designed for extraction from Web text. NELL uses a coupled semi-supervised bootstrapping approach to learn new facts from text, given an initial ontology and a small number of “seeds” for each ontology category. In contrast to previous applications of NELL, in our task the initial ontology and seeds are automatically derived from existing resources. We show that NELL’s bootstrapping algorithm is susceptible to ambiguous seeds, which are frequent in the biomedical domain. Using NELL to extract facts from biomedical text quickly leads to semantic drift. To address this problem, we introduce a method for assessing seed quality, based on a larger corpus of data derived from the Web. In our method, seed quality is assessed at each iteration of the bootstrapping process. Experimental results show significant improvements over NELL’s original bootstrapping algorithm on two types of tasks: learning terms from biomedical categories, and named-entity recognition for biomedical entities using a learned lexicon.
Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text
(2015)

Biomedical named entity recognition (bio-NER) is a crucial and basic step in many biomedical information extraction tasks. However, traditional NER systems are mainly based on complex hand-designed features which are derived from various linguistic analyses and maybe only adapted to specified area. In this paper, we construct Recurrent Neural Network to identify entity names with word embeddings input rather than hand-designed features. Our contributions mainly include three aspects: 1) we adapt a deep learning architecture Recurrent Neural Network (RNN) to entity names recognition; 2) based on the original RNNs such as Elman-type and Jordan-type model, an improved RNN model is proposed; 3) considering that both past and future dependencies are important information, we combine bidirectional recurrent neural networks based on information entropy at the top layer. The experiments conducted on the BioCreative II GM data set demonstrate RNN models outperform CRF and deep neural networks (DNN), furthermore, the improved RNN model performs better than two original RNN models and the combined method is effective.
Extracting Complex Biological Events with Rich Graph-Based Feature Sets
(2009)

We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP’09 Shared Task on Event Extraction. For each event, its text trigger, class, and arguments are extracted. In contrast to the prevailing approaches in the domain, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule-based post-processing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F-score of 51.95% on the primary task, the best performance among the participants.
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
(2015)

Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data. However, the heuristic alignment can fail, resulting in wrong label problem. In addition, in previous approaches, statistical models have typically been applied to ad hoc features. The noise that originates from the feature extraction process can cause poor performance. In this paper, we propose a novel model dubbed the Piecewise Convolutional Neural Networks (PCNNs) with multi-instance learning to address these two problems. To solve the first problem, distant supervised relation extraction is treated as a multi-instance problem in which the uncertainty of instance labels is taken into account. To address the latter problem, we avoid feature engineering and instead adopt convolutional architecture with piecewise max pooling to automatically learn relevant features. Experiments show that our method is effective and outperforms several competitive baseline methods.
Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking
(2005)

Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a coarse-to-fine generative parser (Charniak, 2000). This method generates 50-best lists that are of substantially higher quality than previously obtainable. We used these parses as the input to a MaxEnt reranker (Johnson et al., 1999; Riezler et al., 2002) that selects the best parse from the set of parses for each sentence, obtaining an f-score of 91.0% on sentences of length 100 or less.
Requirements of Quality of Service in Wireless Sensor Network
(2006)

Sensor networks are distributed networks made up of small sensing devices equipped with processors, memory, and short-range wireless communication. They differ from traditional computer networks in that they have resource constraints, unbalanced mixture traffic, data redundancy, network dynamics, and energy balance. Work within wireless sensor networks (WSNs) Quality of service (QoS) has been isolated and specific either on certain functional layers or application scenarios. However the area of sensor network quality of service (QoS) remains largely open. In this paper we define WSNs QoS requirements within a WSNs application, and then analyzing Issues for QoS Monitoring.
The critical role of retrieval practice in long-term retention
(2011)

Learning is usually thought to occur during episodes of studying, whereas retrieval of information on testing simply serves to assess what was learned. We review research that contradicts this traditional view by demonstrating that retrieval practice is actually a powerful mnemonic enhancer, often producing large gains in long-term retention relative to repeated studying. Retrieval practice is often effective even without feedback (i.e. giving the correct answer), but feedback enhances the benefits of testing. In addition, retrieval practice promotes the acquisition of knowledge that can be flexibly retrieved and transferred to different contexts. The power of retrieval practice in consolidating memories has important implications for both the study of memory and its application to educational practice.
An analysis of unreliability and asymmetry in low-power wireless links
(2007)

Experimental studies have demonstrated that the behavior of real links in low-power wireless networks (such as wireless sensor networks) deviates to a large extent from the ideal binary model used in several simulation studies. In particular, there is a large transitional region in wireless link quality that is characterized by significant levels of unreliability and asymmetry, significantly impacting the performance of higher-layer protocols. We provide a comprehensive analysis of the root causes of unreliability and asymmetry. In particular, we derive expressions for the distribution, expectation, and variance of the packet reception rate as a function of distance, as well as for the location and extent of the transitional region. These expressions incorporate important environmental and radio parameters such as the path loss exponent and shadowing variance of the channel, and the modulation, encoding, and hardware variance of the radios.
An LED Driver With Pulse Current Driving Technique
(2012)

A novel driving technique, called pulse current driving technique for LED driver, is proposed. A complete introduction and analysis of the proposed driving technique and the conventional driving technique will be presented in this paper. The LED driver supply maximum peak current is 220 mA and operating frequency is between 500 kHz and1 MHz. Simulation and experimental results are presented to verify the analytical results. The LED driver is fabricated with CMOS 0.35-μm technology and chip area with pads is 0.89 mm 2. The design of the proposed circuit structure is simple, without additional output capacitance compensation for stability and with small power consumption, and is implemented in integrated circuit. As a result of the above benefits, the proposed approach is believed to be suitable in portable application.
Flow Motifs in Soccer : What can passing behavior tell us ?
(2017)

In soccer, both individual and team performance is crucial to win matches. Passing is the backbone of the game and forms the basis of important decisions made by managers and owners; such as buying players, picking offensive or defensive strategies or even defining a style of play. These decisions can be supported by analyzing how a player performs and how his style affects team performance. The flow of a player or a team can be studied by finding unique passing motifs from the patterns in the subgraphs of a possession-passing network of soccer games. These flow motifs can be used to analyze individual players and teams based on the diversity and frequency of their involvement in different motifs. Building on the flow motif analyses, we introduce an expected goals model to measure the effectiveness of each style of play. We also make use of a novel way to represent motif data that is easy to understand and can be used to compare players, teams and seasons. Further, we exploit the relationship between play style and the pass probability matrix to support our analysis. Our data set has the last 4 seasons of 6 big European leagues with 8219 matches, 3532 unique players and 155 unique teams. We will use flow motifs to analyze different events, such as for example the transfer of Claudio Bravo to Pep Guardiola’s Manchester City, who Jean Seri is and why he must be an elite midfielder and the difference in attacking style between Lionel Messi and Cristiano Ronaldo. Ultimately, an analysis of Post-Fàbregas Arsenal is conducted wherein different techniques are combined to analyze the impact the acquisition of Mesut Özil and Alexis Sánchez had on the strategies implemented at Arsenal.